Modular Architectures and Entanglement Schemes for Error-Corrected Distributed Quantum Computation

TL;DR

This paper analyzes modular quantum computing architectures using solid-state hardware, focusing on entanglement schemes and error correction thresholds to guide scalable quantum system design.

Contribution

It compares entanglement schemes and modular architectures, linking physical hardware parameters to error correction performance for fault-tolerant quantum computing.

Findings

Entanglement scheme choice significantly affects error correction thresholds.

Two modular architectures have similar error-correcting thresholds.

Future hardware parameters could achieve thresholds near non-distributed systems (~0.4%).

Abstract

Connecting multiple smaller qubit modules by generating high-fidelity entangled states is a promising path for scaling quantum computing hardware. The performance of such a modular quantum computer is highly dependent on the quality and rate of entanglement generation. However, the optimal architectures and entanglement generation schemes are not yet established. Focusing on modular quantum computers with solid-state quantum hardware, we investigate a distributed surface code's error-correcting threshold and logical failure rate. We consider both emission-based and scattering-based entanglement generation schemes for the measurement of non-local stabilizers. Through quantum optical modeling, we link the performance of the quantum error correction code to the parameters of the underlying physical hardware and identify the necessary parameter regime for fault-tolerant modular quantum computation. In addition, we compare modular architectures with one or two data qubits per module. We find that the performance of the code depends significantly on the choice of entanglement generation scheme, while the two modular architectures have similar error-correcting thresholds. For some schemes, thresholds nearing the thresholds of non-distributed implementations ($\sim0.4 \%$) appear feasible with future parameters.